{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import datetime\n",
    "import encodings\n",
    "\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from matplotlib.offsetbox import AnchoredText\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('A:\\\\Data Analysis Jupyter\\\\Water-Quality-Analysis\\\\data.csv', encoding = \"latin1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>District Name</th>\n",
       "      <th>Block Name</th>\n",
       "      <th>Panchayat Name</th>\n",
       "      <th>Village Name</th>\n",
       "      <th>Habitation Name</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GOKAVARAM(04)</td>\n",
       "      <td>VANTHADA(014 )</td>\n",
       "      <td>VANTHADA(0404410014010400)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>1/4/2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GOKAVARAM(04)</td>\n",
       "      <td>PANDAVULAPALEM(022 )</td>\n",
       "      <td>PANDAVULAPALEM(0404410022010400)</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>1/4/2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GAJJANAPUDI(06)</td>\n",
       "      <td>G. KOTHURU(023 )</td>\n",
       "      <td>G. KOTHURU(0404410023010600)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>1/4/2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Name      District Name      Block Name   Panchayat Name  \\\n",
       "0  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)    GOKAVARAM(04)   \n",
       "1  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)    GOKAVARAM(04)   \n",
       "2  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)  GAJJANAPUDI(06)   \n",
       "\n",
       "           Village Name                   Habitation Name Quality Parameter  \\\n",
       "0        VANTHADA(014 )        VANTHADA(0404410014010400)          Salinity   \n",
       "1  PANDAVULAPALEM(022 )  PANDAVULAPALEM(0404410022010400)          Fluoride   \n",
       "2      G. KOTHURU(023 )      G. KOTHURU(0404410023010600)          Salinity   \n",
       "\n",
       "       Year  \n",
       "0  1/4/2009  \n",
       "1  1/4/2009  \n",
       "2  1/4/2009  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year'] = pd.to_datetime(df['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Session'] = df['Year'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>District Name</th>\n",
       "      <th>Block Name</th>\n",
       "      <th>Panchayat Name</th>\n",
       "      <th>Village Name</th>\n",
       "      <th>Habitation Name</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>Year</th>\n",
       "      <th>Session</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GOKAVARAM(04)</td>\n",
       "      <td>VANTHADA(014 )</td>\n",
       "      <td>VANTHADA(0404410014010400)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>2009-01-04</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GOKAVARAM(04)</td>\n",
       "      <td>PANDAVULAPALEM(022 )</td>\n",
       "      <td>PANDAVULAPALEM(0404410022010400)</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>2009-01-04</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GAJJANAPUDI(06)</td>\n",
       "      <td>G. KOTHURU(023 )</td>\n",
       "      <td>G. KOTHURU(0404410023010600)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>2009-01-04</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GAJJANAPUDI(06)</td>\n",
       "      <td>GAJJANAPUDI(029 )</td>\n",
       "      <td>GAJJANAPUDI(0404410029010600)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>2009-01-04</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>CHINTALURU(10)</td>\n",
       "      <td>CHINTALURU(028 )</td>\n",
       "      <td>CHINTALURU(0404410028011000)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>2009-01-04</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Name      District Name      Block Name   Panchayat Name  \\\n",
       "0  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)    GOKAVARAM(04)   \n",
       "1  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)    GOKAVARAM(04)   \n",
       "2  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)  GAJJANAPUDI(06)   \n",
       "3  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)  GAJJANAPUDI(06)   \n",
       "4  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)   CHINTALURU(10)   \n",
       "\n",
       "           Village Name                   Habitation Name Quality Parameter  \\\n",
       "0        VANTHADA(014 )        VANTHADA(0404410014010400)          Salinity   \n",
       "1  PANDAVULAPALEM(022 )  PANDAVULAPALEM(0404410022010400)          Fluoride   \n",
       "2      G. KOTHURU(023 )      G. KOTHURU(0404410023010600)          Salinity   \n",
       "3     GAJJANAPUDI(029 )     GAJJANAPUDI(0404410029010600)          Salinity   \n",
       "4      CHINTALURU(028 )      CHINTALURU(0404410028011000)          Salinity   \n",
       "\n",
       "        Year  Session  \n",
       "0 2009-01-04     2009  \n",
       "1 2009-01-04     2009  \n",
       "2 2009-01-04     2009  \n",
       "3 2009-01-04     2009  \n",
       "4 2009-01-04     2009  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Year',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>State Name</th>\n",
       "      <th>District Name</th>\n",
       "      <th>Block Name</th>\n",
       "      <th>Panchayat Name</th>\n",
       "      <th>Village Name</th>\n",
       "      <th>Habitation Name</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>Session</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GOKAVARAM(04)</td>\n",
       "      <td>VANTHADA(014 )</td>\n",
       "      <td>VANTHADA(0404410014010400)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
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       "      <td>2009</td>\n",
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       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GAJJANAPUDI(06)</td>\n",
       "      <td>G. KOTHURU(023 )</td>\n",
       "      <td>G. KOTHURU(0404410023010600)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GAJJANAPUDI(06)</td>\n",
       "      <td>GAJJANAPUDI(029 )</td>\n",
       "      <td>GAJJANAPUDI(0404410029010600)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>CHINTALURU(10)</td>\n",
       "      <td>CHINTALURU(028 )</td>\n",
       "      <td>CHINTALURU(0404410028011000)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Name      District Name      Block Name   Panchayat Name  \\\n",
       "0  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)    GOKAVARAM(04)   \n",
       "1  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)    GOKAVARAM(04)   \n",
       "2  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)  GAJJANAPUDI(06)   \n",
       "3  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)  GAJJANAPUDI(06)   \n",
       "4  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)   CHINTALURU(10)   \n",
       "\n",
       "           Village Name                   Habitation Name Quality Parameter  \\\n",
       "0        VANTHADA(014 )        VANTHADA(0404410014010400)          Salinity   \n",
       "1  PANDAVULAPALEM(022 )  PANDAVULAPALEM(0404410022010400)          Fluoride   \n",
       "2      G. KOTHURU(023 )      G. KOTHURU(0404410023010600)          Salinity   \n",
       "3     GAJJANAPUDI(029 )     GAJJANAPUDI(0404410029010600)          Salinity   \n",
       "4      CHINTALURU(028 )      CHINTALURU(0404410028011000)          Salinity   \n",
       "\n",
       "   Session  \n",
       "0     2009  \n",
       "1     2009  \n",
       "2     2009  \n",
       "3     2009  \n",
       "4     2009  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'Session':'Year'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>District Name</th>\n",
       "      <th>Block Name</th>\n",
       "      <th>Panchayat Name</th>\n",
       "      <th>Village Name</th>\n",
       "      <th>Habitation Name</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GOKAVARAM(04)</td>\n",
       "      <td>VANTHADA(014 )</td>\n",
       "      <td>VANTHADA(0404410014010400)</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI(04)</td>\n",
       "      <td>PRATHIPADU(10)</td>\n",
       "      <td>GOKAVARAM(04)</td>\n",
       "      <td>PANDAVULAPALEM(022 )</td>\n",
       "      <td>PANDAVULAPALEM(0404410022010400)</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Name      District Name      Block Name Panchayat Name  \\\n",
       "0  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)  GOKAVARAM(04)   \n",
       "1  ANDHRA PRADESH  EAST GODAVARI(04)  PRATHIPADU(10)  GOKAVARAM(04)   \n",
       "\n",
       "           Village Name                   Habitation Name Quality Parameter  \\\n",
       "0        VANTHADA(014 )        VANTHADA(0404410014010400)          Salinity   \n",
       "1  PANDAVULAPALEM(022 )  PANDAVULAPALEM(0404410022010400)          Fluoride   \n",
       "\n",
       "   Year  \n",
       "0  2009  \n",
       "1  2009  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"updated-wq.csv\", index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State Name', 'District Name', 'Block Name', 'Panchayat Name',\n",
       "       'Village Name', 'Habitation Name', 'Quality Parameter', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ANDHRA PRADESH', 'ASSAM', 'ARUNACHAL PRADESH', 'BIHAR', 'GUJARAT',\n",
       "       'HARYANA', 'HIMACHAL PRADESH', 'JAMMU AND KASHMIR', 'KARNATAKA',\n",
       "       'KERALA', 'MADHYA PRADESH', 'MAHARASHTRA', 'MEGHALAYA', 'NAGALAND',\n",
       "       'ORISSA', 'PUDUCHERRY', 'PUNJAB', 'RAJASTHAN', 'TAMIL NADU',\n",
       "       'TRIPURA', 'UTTAR PRADESH', 'WEST BENGAL', 'CHATTISGARH',\n",
       "       'JHARKHAND', 'UTTARAKHAND', 'MANIPUR', 'CHHATTISGARH'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['State Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['State Name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ANDHRA PRADESH</th>\n",
       "      <td>2888</td>\n",
       "      <td>2</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>2193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARUNACHAL PRADESH</th>\n",
       "      <td>612</td>\n",
       "      <td>1</td>\n",
       "      <td>Iron</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSAM</th>\n",
       "      <td>79910</td>\n",
       "      <td>3</td>\n",
       "      <td>Iron</td>\n",
       "      <td>74098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIHAR</th>\n",
       "      <td>92336</td>\n",
       "      <td>4</td>\n",
       "      <td>Iron</td>\n",
       "      <td>69970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHATTISGARH</th>\n",
       "      <td>25062</td>\n",
       "      <td>5</td>\n",
       "      <td>Iron</td>\n",
       "      <td>24439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHHATTISGARH</th>\n",
       "      <td>8815</td>\n",
       "      <td>3</td>\n",
       "      <td>Iron</td>\n",
       "      <td>8339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUJARAT</th>\n",
       "      <td>2092</td>\n",
       "      <td>4</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HARYANA</th>\n",
       "      <td>262</td>\n",
       "      <td>2</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIMACHAL PRADESH</th>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAMMU AND KASHMIR</th>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JHARKHAND</th>\n",
       "      <td>3913</td>\n",
       "      <td>5</td>\n",
       "      <td>Iron</td>\n",
       "      <td>3254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KARNATAKA</th>\n",
       "      <td>30824</td>\n",
       "      <td>5</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>13156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KERALA</th>\n",
       "      <td>4800</td>\n",
       "      <td>4</td>\n",
       "      <td>Iron</td>\n",
       "      <td>3161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MADHYA PRADESH</th>\n",
       "      <td>14449</td>\n",
       "      <td>4</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>12762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAHARASHTRA</th>\n",
       "      <td>12480</td>\n",
       "      <td>5</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>4184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANIPUR</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>Iron</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEGHALAYA</th>\n",
       "      <td>427</td>\n",
       "      <td>2</td>\n",
       "      <td>Iron</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAGALAND</th>\n",
       "      <td>618</td>\n",
       "      <td>1</td>\n",
       "      <td>Iron</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORISSA</th>\n",
       "      <td>68620</td>\n",
       "      <td>5</td>\n",
       "      <td>Iron</td>\n",
       "      <td>59905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUDUCHERRY</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>Iron</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUNJAB</th>\n",
       "      <td>1056</td>\n",
       "      <td>3</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAJASTHAN</th>\n",
       "      <td>131417</td>\n",
       "      <td>5</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>87137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAMIL NADU</th>\n",
       "      <td>3164</td>\n",
       "      <td>4</td>\n",
       "      <td>Iron</td>\n",
       "      <td>2119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRIPURA</th>\n",
       "      <td>26235</td>\n",
       "      <td>1</td>\n",
       "      <td>Iron</td>\n",
       "      <td>26235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTTAR PRADESH</th>\n",
       "      <td>9918</td>\n",
       "      <td>5</td>\n",
       "      <td>Iron</td>\n",
       "      <td>3376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTTARAKHAND</th>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>Iron</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEST BENGAL</th>\n",
       "      <td>30101</td>\n",
       "      <td>4</td>\n",
       "      <td>Arsenic</td>\n",
       "      <td>12382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count unique       top   freq\n",
       "State Name                                       \n",
       "ANDHRA PRADESH       2888      2  Fluoride   2193\n",
       "ARUNACHAL PRADESH     612      1      Iron    612\n",
       "ASSAM               79910      3      Iron  74098\n",
       "BIHAR               92336      4      Iron  69970\n",
       "CHATTISGARH         25062      5      Iron  24439\n",
       "CHHATTISGARH         8815      3      Iron   8339\n",
       "GUJARAT              2092      4  Fluoride    804\n",
       "HARYANA               262      2  Fluoride    244\n",
       "HIMACHAL PRADESH       88      3  Salinity     72\n",
       "JAMMU AND KASHMIR      67      3  Salinity     40\n",
       "JHARKHAND            3913      5      Iron   3254\n",
       "KARNATAKA           30824      5  Fluoride  13156\n",
       "KERALA               4800      4      Iron   3161\n",
       "MADHYA PRADESH      14449      4  Fluoride  12762\n",
       "MAHARASHTRA         12480      5  Fluoride   4184\n",
       "MANIPUR                14      1      Iron     14\n",
       "MEGHALAYA             427      2      Iron    425\n",
       "NAGALAND              618      1      Iron    618\n",
       "ORISSA              68620      5      Iron  59905\n",
       "PUDUCHERRY             17      2      Iron     16\n",
       "PUNJAB               1056      3  Salinity    894\n",
       "RAJASTHAN          131417      5  Salinity  87137\n",
       "TAMIL NADU           3164      4      Iron   2119\n",
       "TRIPURA             26235      1      Iron  26235\n",
       "UTTAR PRADESH        9918      5      Iron   3376\n",
       "UTTARAKHAND            57      4      Iron     35\n",
       "WEST BENGAL         30101      4   Arsenic  12382"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quality Parameter'].groupby(df['State Name']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>State Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ANDHRA PRADESH</th>\n",
       "      <td>2888.0</td>\n",
       "      <td>2010.096953</td>\n",
       "      <td>1.059186</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
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       "    <tr>\n",
       "      <th>ARUNACHAL PRADESH</th>\n",
       "      <td>612.0</td>\n",
       "      <td>2009.928105</td>\n",
       "      <td>1.093678</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSAM</th>\n",
       "      <td>79910.0</td>\n",
       "      <td>2010.300989</td>\n",
       "      <td>1.129517</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIHAR</th>\n",
       "      <td>92336.0</td>\n",
       "      <td>2010.137303</td>\n",
       "      <td>1.091053</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHATTISGARH</th>\n",
       "      <td>25062.0</td>\n",
       "      <td>2009.978693</td>\n",
       "      <td>0.804317</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHHATTISGARH</th>\n",
       "      <td>8815.0</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUJARAT</th>\n",
       "      <td>2092.0</td>\n",
       "      <td>2009.963193</td>\n",
       "      <td>1.063315</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HARYANA</th>\n",
       "      <td>262.0</td>\n",
       "      <td>2009.561069</td>\n",
       "      <td>0.931614</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIMACHAL PRADESH</th>\n",
       "      <td>88.0</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAMMU AND KASHMIR</th>\n",
       "      <td>67.0</td>\n",
       "      <td>2011.194030</td>\n",
       "      <td>0.925052</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2012.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JHARKHAND</th>\n",
       "      <td>3913.0</td>\n",
       "      <td>2010.208791</td>\n",
       "      <td>0.890247</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KARNATAKA</th>\n",
       "      <td>30824.0</td>\n",
       "      <td>2010.350052</td>\n",
       "      <td>1.078933</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KERALA</th>\n",
       "      <td>4800.0</td>\n",
       "      <td>2010.199583</td>\n",
       "      <td>1.154171</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MADHYA PRADESH</th>\n",
       "      <td>14449.0</td>\n",
       "      <td>2010.215240</td>\n",
       "      <td>1.140364</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAHARASHTRA</th>\n",
       "      <td>12480.0</td>\n",
       "      <td>2010.164343</td>\n",
       "      <td>1.021993</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANIPUR</th>\n",
       "      <td>14.0</td>\n",
       "      <td>2009.928571</td>\n",
       "      <td>0.828742</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010.75</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEGHALAYA</th>\n",
       "      <td>427.0</td>\n",
       "      <td>2010.442623</td>\n",
       "      <td>1.097740</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.5</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAGALAND</th>\n",
       "      <td>618.0</td>\n",
       "      <td>2010.435275</td>\n",
       "      <td>1.084674</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORISSA</th>\n",
       "      <td>68620.0</td>\n",
       "      <td>2010.234115</td>\n",
       "      <td>1.110266</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUDUCHERRY</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2010.823529</td>\n",
       "      <td>1.333946</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUNJAB</th>\n",
       "      <td>1056.0</td>\n",
       "      <td>2009.296402</td>\n",
       "      <td>0.707593</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAJASTHAN</th>\n",
       "      <td>131417.0</td>\n",
       "      <td>2010.364869</td>\n",
       "      <td>1.100744</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAMIL NADU</th>\n",
       "      <td>3164.0</td>\n",
       "      <td>2010.293300</td>\n",
       "      <td>0.971588</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRIPURA</th>\n",
       "      <td>26235.0</td>\n",
       "      <td>2010.417915</td>\n",
       "      <td>1.112283</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTTAR PRADESH</th>\n",
       "      <td>9918.0</td>\n",
       "      <td>2009.686530</td>\n",
       "      <td>0.978874</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTTARAKHAND</th>\n",
       "      <td>57.0</td>\n",
       "      <td>2010.684211</td>\n",
       "      <td>1.071679</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2012.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEST BENGAL</th>\n",
       "      <td>30101.0</td>\n",
       "      <td>2010.188333</td>\n",
       "      <td>1.109358</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count         mean       std     min     25%     50%  \\\n",
       "State Name                                                                   \n",
       "ANDHRA PRADESH       2888.0  2010.096953  1.059186  2009.0  2009.0  2010.0   \n",
       "ARUNACHAL PRADESH     612.0  2009.928105  1.093678  2009.0  2009.0  2010.0   \n",
       "ASSAM               79910.0  2010.300989  1.129517  2009.0  2009.0  2010.0   \n",
       "BIHAR               92336.0  2010.137303  1.091053  2009.0  2009.0  2010.0   \n",
       "CHATTISGARH         25062.0  2009.978693  0.804317  2009.0  2009.0  2010.0   \n",
       "CHHATTISGARH         8815.0  2012.000000  0.000000  2012.0  2012.0  2012.0   \n",
       "GUJARAT              2092.0  2009.963193  1.063315  2009.0  2009.0  2010.0   \n",
       "HARYANA               262.0  2009.561069  0.931614  2009.0  2009.0  2009.0   \n",
       "HIMACHAL PRADESH       88.0  2009.000000  0.000000  2009.0  2009.0  2009.0   \n",
       "JAMMU AND KASHMIR      67.0  2011.194030  0.925052  2009.0  2011.0  2011.0   \n",
       "JHARKHAND            3913.0  2010.208791  0.890247  2009.0  2010.0  2010.0   \n",
       "KARNATAKA           30824.0  2010.350052  1.078933  2009.0  2009.0  2010.0   \n",
       "KERALA               4800.0  2010.199583  1.154171  2009.0  2009.0  2010.0   \n",
       "MADHYA PRADESH      14449.0  2010.215240  1.140364  2009.0  2009.0  2010.0   \n",
       "MAHARASHTRA         12480.0  2010.164343  1.021993  2009.0  2009.0  2010.0   \n",
       "MANIPUR                14.0  2009.928571  0.828742  2009.0  2009.0  2010.0   \n",
       "MEGHALAYA             427.0  2010.442623  1.097740  2009.0  2009.5  2010.0   \n",
       "NAGALAND              618.0  2010.435275  1.084674  2009.0  2009.0  2010.0   \n",
       "ORISSA              68620.0  2010.234115  1.110266  2009.0  2009.0  2010.0   \n",
       "PUDUCHERRY             17.0  2010.823529  1.333946  2009.0  2010.0  2012.0   \n",
       "PUNJAB               1056.0  2009.296402  0.707593  2009.0  2009.0  2009.0   \n",
       "RAJASTHAN          131417.0  2010.364869  1.100744  2009.0  2009.0  2010.0   \n",
       "TAMIL NADU           3164.0  2010.293300  0.971588  2009.0  2010.0  2010.0   \n",
       "TRIPURA             26235.0  2010.417915  1.112283  2009.0  2009.0  2010.0   \n",
       "UTTAR PRADESH        9918.0  2009.686530  0.978874  2009.0  2009.0  2009.0   \n",
       "UTTARAKHAND            57.0  2010.684211  1.071679  2009.0  2010.0  2011.0   \n",
       "WEST BENGAL         30101.0  2010.188333  1.109358  2009.0  2009.0  2010.0   \n",
       "\n",
       "                       75%     max  \n",
       "State Name                          \n",
       "ANDHRA PRADESH     2011.00  2012.0  \n",
       "ARUNACHAL PRADESH  2010.00  2012.0  \n",
       "ASSAM              2011.00  2012.0  \n",
       "BIHAR              2011.00  2012.0  \n",
       "CHATTISGARH        2011.00  2011.0  \n",
       "CHHATTISGARH       2012.00  2012.0  \n",
       "GUJARAT            2011.00  2012.0  \n",
       "HARYANA            2010.00  2012.0  \n",
       "HIMACHAL PRADESH   2009.00  2009.0  \n",
       "JAMMU AND KASHMIR  2012.00  2012.0  \n",
       "JHARKHAND          2011.00  2012.0  \n",
       "KARNATAKA          2011.00  2012.0  \n",
       "KERALA             2011.00  2012.0  \n",
       "MADHYA PRADESH     2011.00  2012.0  \n",
       "MAHARASHTRA        2011.00  2012.0  \n",
       "MANIPUR            2010.75  2011.0  \n",
       "MEGHALAYA          2011.00  2012.0  \n",
       "NAGALAND           2011.00  2012.0  \n",
       "ORISSA             2011.00  2012.0  \n",
       "PUDUCHERRY         2012.00  2012.0  \n",
       "PUNJAB             2009.00  2012.0  \n",
       "RAJASTHAN          2011.00  2012.0  \n",
       "TAMIL NADU         2011.00  2012.0  \n",
       "TRIPURA            2011.00  2012.0  \n",
       "UTTAR PRADESH      2010.00  2012.0  \n",
       "UTTARAKHAND        2012.00  2012.0  \n",
       "WEST BENGAL        2011.00  2012.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Year'].groupby(df['State Name']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "descQ = df['Quality Parameter'].groupby(df['State Name']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "descQ.to_csv('description.csv', index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>Quality Parameter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>Salinity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>Fluoride</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Name Quality Parameter\n",
       "0  ANDHRA PRADESH          Salinity\n",
       "1  ANDHRA PRADESH          Fluoride"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[['State Name', 'Quality Parameter']]\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "number = LabelEncoder()\n",
    "data['Quality'] = number.fit_transform(data['Quality Parameter'].astype('str'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Name Quality Parameter  Quality\n",
       "0  ANDHRA PRADESH          Salinity        4\n",
       "1  ANDHRA PRADESH          Fluoride        1\n",
       "2  ANDHRA PRADESH          Salinity        4\n",
       "3  ANDHRA PRADESH          Salinity        4\n",
       "4  ANDHRA PRADESH          Salinity        4"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "set1 = data.groupby(['State Name', 'Quality Parameter','Quality']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State Name</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ANDHRA PRADESH</th>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARUNACHAL PRADESH</th>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">ASSAM</th>\n",
       "      <th>Arsenic</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fluoride</th>\n",
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       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">BIHAR</th>\n",
       "      <th>Arsenic</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitrate</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">CHATTISGARH</th>\n",
       "      <th>Arsenic</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitrate</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CHHATTISGARH</th>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">GUJARAT</th>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitrate</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HARYANA</th>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">HIMACHAL PRADESH</th>\n",
       "      <th>Arsenic</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">JAMMU AND KASHMIR</th>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ORISSA</th>\n",
       "      <th>Nitrate</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PUDUCHERRY</th>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PUNJAB</th>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">RAJASTHAN</th>\n",
       "      <th>Arsenic</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitrate</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">TAMIL NADU</th>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitrate</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRIPURA</th>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">UTTAR PRADESH</th>\n",
       "      <th>Arsenic</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitrate</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">UTTARAKHAND</th>\n",
       "      <th>Arsenic</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitrate</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">WEST BENGAL</th>\n",
       "      <th>Arsenic</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fluoride</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 0 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [(ANDHRA PRADESH, Fluoride, 1), (ANDHRA PRADESH, Salinity, 4), (ARUNACHAL PRADESH, Iron, 2), (ASSAM, Arsenic, 0), (ASSAM, Fluoride, 1), (ASSAM, Iron, 2), (BIHAR, Arsenic, 0), (BIHAR, Fluoride, 1), (BIHAR, Iron, 2), (BIHAR, Nitrate, 3), (CHATTISGARH, Arsenic, 0), (CHATTISGARH, Fluoride, 1), (CHATTISGARH, Iron, 2), (CHATTISGARH, Nitrate, 3), (CHATTISGARH, Salinity, 4), (CHHATTISGARH, Fluoride, 1), (CHHATTISGARH, Iron, 2), (CHHATTISGARH, Salinity, 4), (GUJARAT, Fluoride, 1), (GUJARAT, Iron, 2), (GUJARAT, Nitrate, 3), (GUJARAT, Salinity, 4), (HARYANA, Fluoride, 1), (HARYANA, Salinity, 4), (HIMACHAL PRADESH, Arsenic, 0), (HIMACHAL PRADESH, Iron, 2), (HIMACHAL PRADESH, Salinity, 4), (JAMMU AND KASHMIR, Fluoride, 1), (JAMMU AND KASHMIR, Iron, 2), (JAMMU AND KASHMIR, Salinity, 4), (JHARKHAND, Arsenic, 0), (JHARKHAND, Fluoride, 1), (JHARKHAND, Iron, 2), (JHARKHAND, Nitrate, 3), (JHARKHAND, Salinity, 4), (KARNATAKA, Arsenic, 0), (KARNATAKA, Fluoride, 1), (KARNATAKA, Iron, 2), (KARNATAKA, Nitrate, 3), (KARNATAKA, Salinity, 4), (KERALA, Fluoride, 1), (KERALA, Iron, 2), (KERALA, Nitrate, 3), (KERALA, Salinity, 4), (MADHYA PRADESH, Fluoride, 1), (MADHYA PRADESH, Iron, 2), (MADHYA PRADESH, Nitrate, 3), (MADHYA PRADESH, Salinity, 4), (MAHARASHTRA, Arsenic, 0), (MAHARASHTRA, Fluoride, 1), (MAHARASHTRA, Iron, 2), (MAHARASHTRA, Nitrate, 3), (MAHARASHTRA, Salinity, 4), (MANIPUR, Iron, 2), (MEGHALAYA, Fluoride, 1), (MEGHALAYA, Iron, 2), (NAGALAND, Iron, 2), (ORISSA, Arsenic, 0), (ORISSA, Fluoride, 1), (ORISSA, Iron, 2), (ORISSA, Nitrate, 3), (ORISSA, Salinity, 4), (PUDUCHERRY, Iron, 2), (PUDUCHERRY, Salinity, 4), (PUNJAB, Fluoride, 1), (PUNJAB, Iron, 2), (PUNJAB, Salinity, 4), (RAJASTHAN, Arsenic, 0), (RAJASTHAN, Fluoride, 1), (RAJASTHAN, Iron, 2), (RAJASTHAN, Nitrate, 3), (RAJASTHAN, Salinity, 4), (TAMIL NADU, Fluoride, 1), (TAMIL NADU, Iron, 2), (TAMIL NADU, Nitrate, 3), (TAMIL NADU, Salinity, 4), (TRIPURA, Iron, 2), (UTTAR PRADESH, Arsenic, 0), (UTTAR PRADESH, Fluoride, 1), (UTTAR PRADESH, Iron, 2), (UTTAR PRADESH, Nitrate, 3), (UTTAR PRADESH, Salinity, 4), (UTTARAKHAND, Arsenic, 0), (UTTARAKHAND, Fluoride, 1), (UTTARAKHAND, Iron, 2), (UTTARAKHAND, Nitrate, 3), (WEST BENGAL, Arsenic, 0), (WEST BENGAL, Fluoride, 1), (WEST BENGAL, Iron, 2), (WEST BENGAL, Salinity, 4)]\n",
       "\n",
       "[90 rows x 0 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_count = pd.DataFrame({'count' : data.groupby([\"State Name\", \"Quality\",\"Quality Parameter\"]).size()}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "ANDHRA_PRADESH = state_count[state_count[\"State Name\"] == 'ANDHRA PRADESH']\n",
    "ASSAM = state_count[state_count[\"State Name\"] == 'ASSAM']\n",
    "ARUNACHAL_PRADESH = state_count[state_count[\"State Name\"] == 'ARUNACHAL PRADESH']\n",
    "BIHAR = state_count[state_count[\"State Name\"] == 'BIHAR']\n",
    "GUJRAT = state_count[state_count[\"State Name\"] == 'GUJRAT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>1</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>2193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>4</td>\n",
       "      <td>Salinity</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Name  Quality Quality Parameter  count\n",
       "0  ANDHRA PRADESH        1          Fluoride   2193\n",
       "1  ANDHRA PRADESH        4          Salinity    695"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ANDHRA_PRADESH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'WATER QUALITY IN ANDHRA PRADESH')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "ax=sns.barplot(x='count',y='Quality Parameter',data=ANDHRA_PRADESH)\n",
    "ax.set(xlabel='COUNT')\n",
    "sns.despine(left=True,bottom=True)\n",
    "plt.title(\"WATER QUALITY IN ANDHRA PRADESH\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>0</td>\n",
       "      <td>Arsenic</td>\n",
       "      <td>4775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>1</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>1037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>2</td>\n",
       "      <td>Iron</td>\n",
       "      <td>74098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Name  Quality Quality Parameter  count\n",
       "3      ASSAM        0           Arsenic   4775\n",
       "4      ASSAM        1          Fluoride   1037\n",
       "5      ASSAM        2              Iron  74098"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ASSAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'WATER QUALITY IN ASSAM ')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "ax=sns.barplot(x='count',y='Quality Parameter',data=ASSAM)\n",
    "ax.set(xlabel='COUNT')\n",
    "sns.despine(left=True,bottom=True)\n",
    "plt.title(\"WATER QUALITY IN ASSAM \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>2</td>\n",
       "      <td>Iron</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          State Name  Quality Quality Parameter  count\n",
       "2  ARUNACHAL PRADESH        2              Iron    612"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARUNACHAL_PRADESH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'WATER QUALITY IN ARUNACHAL PRADESH ')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "ax=sns.barplot(x='count',y='Quality Parameter',data=ARUNACHAL_PRADESH)\n",
    "ax.set(xlabel='COUNT')\n",
    "sns.despine(left=True,bottom=True)\n",
    "plt.title(\"WATER QUALITY IN ARUNACHAL PRADESH \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>0</td>\n",
       "      <td>Arsenic</td>\n",
       "      <td>6215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>1</td>\n",
       "      <td>Fluoride</td>\n",
       "      <td>16150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>2</td>\n",
       "      <td>Iron</td>\n",
       "      <td>69970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>3</td>\n",
       "      <td>Nitrate</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Name  Quality Quality Parameter  count\n",
       "6      BIHAR        0           Arsenic   6215\n",
       "7      BIHAR        1          Fluoride  16150\n",
       "8      BIHAR        2              Iron  69970\n",
       "9      BIHAR        3           Nitrate      1"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BIHAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'WATER QUALITY IN BIHAR ')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "ax=sns.barplot(x='count',y='Quality Parameter',data=BIHAR)\n",
    "ax.set(xlabel='COUNT')\n",
    "sns.despine(left=True,bottom=True)\n",
    "plt.title(\"WATER QUALITY IN BIHAR \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Quality Parameter</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [State Name, Quality, Quality Parameter, count]\n",
       "Index: []"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GUJRAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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